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mmengine.structures.base_data_element 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Any, Iterator, Optional, Tuple, Type, Union

import numpy as np
import torch


[文档]class BaseDataElement: """A base data interface that supports Tensor-like and dict-like operations. A typical data elements refer to predicted results or ground truth labels on a task, such as predicted bboxes, instance masks, semantic segmentation masks, etc. Because groundtruth labels and predicted results often have similar properties (for example, the predicted bboxes and the groundtruth bboxes), MMEngine uses the same abstract data interface to encapsulate predicted results and groundtruth labels, and it is recommended to use different name conventions to distinguish them, such as using ``gt_instances`` and ``pred_instances`` to distinguish between labels and predicted results. Additionally, we distinguish data elements at instance level, pixel level, and label level. Each of these types has its own characteristics. Therefore, MMEngine defines the base class ``BaseDataElement``, and implement ``InstanceData``, ``PixelData``, and ``LabelData`` inheriting from ``BaseDataElement`` to represent different types of ground truth labels or predictions. Another common data element is sample data. A sample data consists of input data (such as an image) and its annotations and predictions. In general, an image can have multiple types of annotations and/or predictions at the same time (for example, both pixel-level semantic segmentation annotations and instance-level detection bboxes annotations). All labels and predictions of a training sample are often passed between Dataset, Model, Visualizer, and Evaluator components. In order to simplify the interface between components, we can treat them as a large data element and encapsulate them. Such data elements are generally called XXDataSample in the OpenMMLab. Therefore, Similar to `nn.Module`, the `BaseDataElement` allows `BaseDataElement` as its attribute. Such a class generally encapsulates all the data of a sample in the algorithm library, and its attributes generally are various types of data elements. For example, MMDetection is assigned by the BaseDataElement to encapsulate all the data elements of the sample labeling and prediction of a sample in the algorithm library. The attributes in ``BaseDataElement`` are divided into two parts, the ``metainfo`` and the ``data`` respectively. - ``metainfo``: Usually contains the information about the image such as filename, image_shape, pad_shape, etc. The attributes can be accessed or modified by dict-like or object-like operations, such as ``.`` (for data access and modification), ``in``, ``del``, ``pop(str)``, ``get(str)``, ``metainfo_keys()``, ``metainfo_values()``, ``metainfo_items()``, ``set_metainfo()`` (for set or change key-value pairs in metainfo). - ``data``: Annotations or model predictions are stored. The attributes can be accessed or modified by dict-like or object-like operations, such as ``.``, ``in``, ``del``, ``pop(str)``, ``get(str)``, ``keys()``, ``values()``, ``items()``. Users can also apply tensor-like methods to all :obj:`torch.Tensor` in the ``data_fields``, such as ``.cuda()``, ``.cpu()``, ``.numpy()``, ``.to()``, ``to_tensor()``, ``.detach()``. Args: metainfo (dict, optional): A dict contains the meta information of single image, such as ``dict(img_shape=(512, 512, 3), scale_factor=(1, 1, 1, 1))``. Defaults to None. kwargs (dict, optional): A dict contains annotations of single image or model predictions. Defaults to None. Examples: >>> import torch >>> from mmengine.structures import BaseDataElement >>> gt_instances = BaseDataElement() >>> bboxes = torch.rand((5, 4)) >>> scores = torch.rand((5,)) >>> img_id = 0 >>> img_shape = (800, 1333) >>> gt_instances = BaseDataElement( ... metainfo=dict(img_id=img_id, img_shape=img_shape), ... bboxes=bboxes, scores=scores) >>> gt_instances = BaseDataElement( ... metainfo=dict(img_id=img_id, img_shape=(640, 640))) >>> # new >>> gt_instances1 = gt_instances.new( ... metainfo=dict(img_id=1, img_shape=(640, 640)), ... bboxes=torch.rand((5, 4)), ... scores=torch.rand((5,))) >>> gt_instances2 = gt_instances1.new() >>> # add and process property >>> gt_instances = BaseDataElement() >>> gt_instances.set_metainfo(dict(img_id=9, img_shape=(100, 100))) >>> assert 'img_shape' in gt_instances.metainfo_keys() >>> assert 'img_shape' in gt_instances >>> assert 'img_shape' not in gt_instances.keys() >>> assert 'img_shape' in gt_instances.all_keys() >>> print(gt_instances.img_shape) (100, 100) >>> gt_instances.scores = torch.rand((5,)) >>> assert 'scores' in gt_instances.keys() >>> assert 'scores' in gt_instances >>> assert 'scores' in gt_instances.all_keys() >>> assert 'scores' not in gt_instances.metainfo_keys() >>> print(gt_instances.scores) tensor([0.5230, 0.7885, 0.2426, 0.3911, 0.4876]) >>> gt_instances.bboxes = torch.rand((5, 4)) >>> assert 'bboxes' in gt_instances.keys() >>> assert 'bboxes' in gt_instances >>> assert 'bboxes' in gt_instances.all_keys() >>> assert 'bboxes' not in gt_instances.metainfo_keys() >>> print(gt_instances.bboxes) tensor([[0.0900, 0.0424, 0.1755, 0.4469], [0.8648, 0.0592, 0.3484, 0.0913], [0.5808, 0.1909, 0.6165, 0.7088], [0.5490, 0.4209, 0.9416, 0.2374], [0.3652, 0.1218, 0.8805, 0.7523]]) >>> # delete and change property >>> gt_instances = BaseDataElement( ... metainfo=dict(img_id=0, img_shape=(640, 640)), ... bboxes=torch.rand((6, 4)), scores=torch.rand((6,))) >>> gt_instances.set_metainfo(dict(img_shape=(1280, 1280))) >>> gt_instances.img_shape # (1280, 1280) >>> gt_instances.bboxes = gt_instances.bboxes * 2 >>> gt_instances.get('img_shape', None) # (1280, 1280) >>> gt_instances.get('bboxes', None) # 6x4 tensor >>> del gt_instances.img_shape >>> del gt_instances.bboxes >>> assert 'img_shape' not in gt_instances >>> assert 'bboxes' not in gt_instances >>> gt_instances.pop('img_shape', None) # None >>> gt_instances.pop('bboxes', None) # None >>> # Tensor-like >>> cuda_instances = gt_instances.cuda() >>> cuda_instances = gt_instances.to('cuda:0') >>> cpu_instances = cuda_instances.cpu() >>> cpu_instances = cuda_instances.to('cpu') >>> fp16_instances = cuda_instances.to( ... device=None, dtype=torch.float16, non_blocking=False, ... copy=False, memory_format=torch.preserve_format) >>> cpu_instances = cuda_instances.detach() >>> np_instances = cpu_instances.numpy() >>> # print >>> metainfo = dict(img_shape=(800, 1196, 3)) >>> gt_instances = BaseDataElement( ... metainfo=metainfo, det_labels=torch.LongTensor([0, 1, 2, 3])) >>> sample = BaseDataElement(metainfo=metainfo, ... gt_instances=gt_instances) >>> print(sample) <BaseDataElement( META INFORMATION img_shape: (800, 1196, 3) DATA FIELDS gt_instances: <BaseDataElement( META INFORMATION img_shape: (800, 1196, 3) DATA FIELDS det_labels: tensor([0, 1, 2, 3]) ) at 0x7f0ec5eadc70> ) at 0x7f0fea49e130> >>> # inheritance >>> class DetDataSample(BaseDataElement): ... @property ... def proposals(self): ... return self._proposals ... @proposals.setter ... def proposals(self, value): ... self.set_field(value, '_proposals', dtype=BaseDataElement) ... @proposals.deleter ... def proposals(self): ... del self._proposals ... @property ... def gt_instances(self): ... return self._gt_instances ... @gt_instances.setter ... def gt_instances(self, value): ... self.set_field(value, '_gt_instances', ... dtype=BaseDataElement) ... @gt_instances.deleter ... def gt_instances(self): ... del self._gt_instances ... @property ... def pred_instances(self): ... return self._pred_instances ... @pred_instances.setter ... def pred_instances(self, value): ... self.set_field(value, '_pred_instances', ... dtype=BaseDataElement) ... @pred_instances.deleter ... def pred_instances(self): ... del self._pred_instances >>> det_sample = DetDataSample() >>> proposals = BaseDataElement(bboxes=torch.rand((5, 4))) >>> det_sample.proposals = proposals >>> assert 'proposals' in det_sample >>> assert det_sample.proposals == proposals >>> del det_sample.proposals >>> assert 'proposals' not in det_sample >>> with self.assertRaises(AssertionError): ... det_sample.proposals = torch.rand((5, 4)) """ def __init__(self, *, metainfo: Optional[dict] = None, **kwargs) -> None: self._metainfo_fields: set = set() self._data_fields: set = set() if metainfo is not None: self.set_metainfo(metainfo=metainfo) if kwargs: self.set_data(kwargs)
[文档] def set_metainfo(self, metainfo: dict) -> None: """Set or change key-value pairs in ``metainfo_field`` by parameter ``metainfo``. Args: metainfo (dict): A dict contains the meta information of image, such as ``img_shape``, ``scale_factor``, etc. """ assert isinstance( metainfo, dict), f'metainfo should be a ``dict`` but got {type(metainfo)}' meta = copy.deepcopy(metainfo) for k, v in meta.items(): self.set_field(name=k, value=v, field_type='metainfo', dtype=None)
[文档] def set_data(self, data: dict) -> None: """Set or change key-value pairs in ``data_field`` by parameter ``data``. Args: data (dict): A dict contains annotations of image or model predictions. """ assert isinstance(data, dict), f'data should be a `dict` but got {data}' for k, v in data.items(): # Use `setattr()` rather than `self.set_field` to allow `set_data` # to set property method. setattr(self, k, v)
[文档] def update(self, instance: 'BaseDataElement') -> None: """The update() method updates the BaseDataElement with the elements from another BaseDataElement object. Args: instance (BaseDataElement): Another BaseDataElement object for update the current object. """ assert isinstance( instance, BaseDataElement ), f'instance should be a `BaseDataElement` but got {type(instance)}' self.set_metainfo(dict(instance.metainfo_items())) self.set_data(dict(instance.items()))
[文档] def new(self, *, metainfo: Optional[dict] = None, **kwargs) -> 'BaseDataElement': """Return a new data element with same type. If ``metainfo`` and ``data`` are None, the new data element will have same metainfo and data. If metainfo or data is not None, the new result will overwrite it with the input value. Args: metainfo (dict, optional): A dict contains the meta information of image, such as ``img_shape``, ``scale_factor``, etc. Defaults to None. kwargs (dict): A dict contains annotations of image or model predictions. Returns: BaseDataElement: A new data element with same type. """ new_data = self.__class__() if metainfo is not None: new_data.set_metainfo(metainfo) else: new_data.set_metainfo(dict(self.metainfo_items())) if kwargs: new_data.set_data(kwargs) else: new_data.set_data(dict(self.items())) return new_data
[文档] def clone(self): """Deep copy the current data element. Returns: BaseDataElement: The copy of current data element. """ clone_data = self.__class__() clone_data.set_metainfo(dict(self.metainfo_items())) clone_data.set_data(dict(self.items())) return clone_data
[文档] def keys(self) -> list: """ Returns: list: Contains all keys in data_fields. """ # We assume that the name of the attribute related to property is # '_' + the name of the property. We use this rule to filter out # private keys. # TODO: Use a more robust way to solve this problem private_keys = { '_' + key for key in self._data_fields if isinstance(getattr(type(self), key, None), property) } return list(self._data_fields - private_keys)
[文档] def metainfo_keys(self) -> list: """ Returns: list: Contains all keys in metainfo_fields. """ return list(self._metainfo_fields)
[文档] def values(self) -> list: """ Returns: list: Contains all values in data. """ return [getattr(self, k) for k in self.keys()]
[文档] def metainfo_values(self) -> list: """ Returns: list: Contains all values in metainfo. """ return [getattr(self, k) for k in self.metainfo_keys()]
[文档] def all_keys(self) -> list: """ Returns: list: Contains all keys in metainfo and data. """ return self.metainfo_keys() + self.keys()
[文档] def all_values(self) -> list: """ Returns: list: Contains all values in metainfo and data. """ return self.metainfo_values() + self.values()
[文档] def all_items(self) -> Iterator[Tuple[str, Any]]: """ Returns: iterator: An iterator object whose element is (key, value) tuple pairs for ``metainfo`` and ``data``. """ for k in self.all_keys(): yield (k, getattr(self, k))
[文档] def items(self) -> Iterator[Tuple[str, Any]]: """ Returns: iterator: An iterator object whose element is (key, value) tuple pairs for ``data``. """ for k in self.keys(): yield (k, getattr(self, k))
[文档] def metainfo_items(self) -> Iterator[Tuple[str, Any]]: """ Returns: iterator: An iterator object whose element is (key, value) tuple pairs for ``metainfo``. """ for k in self.metainfo_keys(): yield (k, getattr(self, k))
@property def metainfo(self) -> dict: """dict: A dict contains metainfo of current data element.""" return dict(self.metainfo_items()) def __setattr__(self, name: str, value: Any): """setattr is only used to set data.""" if name in ('_metainfo_fields', '_data_fields'): if not hasattr(self, name): super().__setattr__(name, value) else: raise AttributeError(f'{name} has been used as a ' 'private attribute, which is immutable.') else: self.set_field( name=name, value=value, field_type='data', dtype=None) def __delattr__(self, item: str): """Delete the item in dataelement. Args: item (str): The key to delete. """ if item in ('_metainfo_fields', '_data_fields'): raise AttributeError(f'{item} has been used as a ' 'private attribute, which is immutable.') super().__delattr__(item) if item in self._metainfo_fields: self._metainfo_fields.remove(item) elif item in self._data_fields: self._data_fields.remove(item) # dict-like methods __delitem__ = __delattr__
[文档] def get(self, key, default=None) -> Any: """Get property in data and metainfo as the same as python.""" # Use `getattr()` rather than `self.__dict__.get()` to allow getting # properties. return getattr(self, key, default)
[文档] def pop(self, *args) -> Any: """Pop property in data and metainfo as the same as python.""" assert len(args) < 3, '``pop`` get more than 2 arguments' name = args[0] if name in self._metainfo_fields: self._metainfo_fields.remove(args[0]) return self.__dict__.pop(*args) elif name in self._data_fields: self._data_fields.remove(args[0]) return self.__dict__.pop(*args) # with default value elif len(args) == 2: return args[1] else: # don't just use 'self.__dict__.pop(*args)' for only popping key in # metainfo or data raise KeyError(f'{args[0]} is not contained in metainfo or data')
def __contains__(self, item: str) -> bool: """Whether the item is in dataelement. Args: item (str): The key to inquire. """ return item in self._data_fields or item in self._metainfo_fields
[文档] def set_field(self, value: Any, name: str, dtype: Optional[Union[Type, Tuple[Type, ...]]] = None, field_type: str = 'data') -> None: """Special method for set union field, used as property.setter functions.""" assert field_type in ['metainfo', 'data'] if dtype is not None: assert isinstance( value, dtype), f'{value} should be a {dtype} but got {type(value)}' if field_type == 'metainfo': if name in self._data_fields: raise AttributeError( f'Cannot set {name} to be a field of metainfo ' f'because {name} is already a data field') self._metainfo_fields.add(name) else: if name in self._metainfo_fields: raise AttributeError( f'Cannot set {name} to be a field of data ' f'because {name} is already a metainfo field') self._data_fields.add(name) super().__setattr__(name, value)
# Tensor-like methods
[文档] def to(self, *args, **kwargs) -> 'BaseDataElement': """Apply same name function to all tensors in data_fields.""" new_data = self.new() for k, v in self.items(): if hasattr(v, 'to'): v = v.to(*args, **kwargs) data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def cpu(self) -> 'BaseDataElement': """Convert all tensors to CPU in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.cpu() data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def cuda(self) -> 'BaseDataElement': """Convert all tensors to GPU in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.cuda() data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def musa(self) -> 'BaseDataElement': """Convert all tensors to musa in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.musa() data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def npu(self) -> 'BaseDataElement': """Convert all tensors to NPU in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.npu() data = {k: v} new_data.set_data(data) return new_data
[文档] def mlu(self) -> 'BaseDataElement': """Convert all tensors to MLU in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.mlu() data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def detach(self) -> 'BaseDataElement': """Detach all tensors in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.detach() data = {k: v} new_data.set_data(data) return new_data
# Tensor-like methods
[文档] def numpy(self) -> 'BaseDataElement': """Convert all tensors to np.ndarray in data.""" new_data = self.new() for k, v in self.items(): if isinstance(v, (torch.Tensor, BaseDataElement)): v = v.detach().cpu().numpy() data = {k: v} new_data.set_data(data) return new_data
[文档] def to_tensor(self) -> 'BaseDataElement': """Convert all np.ndarray to tensor in data.""" new_data = self.new() for k, v in self.items(): data = {} if isinstance(v, np.ndarray): v = torch.from_numpy(v) data[k] = v elif isinstance(v, BaseDataElement): v = v.to_tensor() data[k] = v new_data.set_data(data) return new_data
[文档] def to_dict(self) -> dict: """Convert BaseDataElement to dict.""" return { k: v.to_dict() if isinstance(v, BaseDataElement) else v for k, v in self.all_items() }
def __repr__(self) -> str: """Represent the object.""" def _addindent(s_: str, num_spaces: int) -> str: """This func is modified from `pytorch` https://github.com/pytorch/ pytorch/blob/b17b2b1cc7b017c3daaeff8cc7ec0f514d42ec37/torch/nn/modu les/module.py#L29. Args: s_ (str): The string to add spaces. num_spaces (int): The num of space to add. Returns: str: The string after add indent. """ s = s_.split('\n') # don't do anything for single-line stuff if len(s) == 1: return s_ first = s.pop(0) s = [(num_spaces * ' ') + line for line in s] s = '\n'.join(s) # type: ignore s = first + '\n' + s # type: ignore return s # type: ignore def dump(obj: Any) -> str: """Represent the object. Args: obj (Any): The obj to represent. Returns: str: The represented str. """ _repr = '' if isinstance(obj, dict): for k, v in obj.items(): _repr += f'\n{k}: {_addindent(dump(v), 4)}' elif isinstance(obj, BaseDataElement): _repr += '\n\n META INFORMATION' metainfo_items = dict(obj.metainfo_items()) _repr += _addindent(dump(metainfo_items), 4) _repr += '\n\n DATA FIELDS' items = dict(obj.items()) _repr += _addindent(dump(items), 4) classname = obj.__class__.__name__ _repr = f'<{classname}({_repr}\n) at {hex(id(obj))}>' else: _repr += repr(obj) return _repr return dump(self)

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